nimg 文件服务器,NIMG-45. DEEP LEARNING-BASED PERITUMORAL MICROSTRUCTURE MAPPING IN GLIOBLASTOMAS USING FR...

摘要：

PURPOSE Characterization of the peritumoral microenvironment is a widely researched, but as yet unsolved problem. Determining the tissue microstructure differences between tumor types, arising from differences in infiltration, edema, and disease driven changes in cellularity is important to be able to guide treatment options. Diffusion tensor imaging with characterization of extracellular free water provides unique information of the tissue microstructure. The goal of this work is to leverage this information by applying deep learning on free water maps and create a microstructure map of the peritumoral area, to aid in targeted resection, radiotherapy and treatment management in the form of a crucial supplemental radiomic feature, replacing all other diffusion derived measures. METHOD We leveraged the widely different peritumoral microenvironments of GBMs and Metastatic tumors to create the microstructure maps. Tumor and peritumoral regions were automatically delineated in 143 patients with brain tumors (89 glioblastomas and 54 metastasis, ages 19-87 years, 77 females), and free water maps were computed in the peritumoral regions using their DTI data. We trained a Convolutional Neural Network (CNN) on 32x32 mm patches in the peritumoral area from GBMs and Mets, labeled as non-enhancing tumor (low free-water) and edema (high free-water), respectively. An independent test set was used and the CNN associated a voxel-wise probability to their peritumoral region to produce microstructure maps of GBMs and Mets which were then statistically compared. RESULT For comparison, a t-test was used showing significant group difference in the microstructure map between Mets and GBMs (p< 0.05). CONCLUSION The voxel-wise microstructure map is able to capture the cellularity differences in the peritumoral region, based on DTI-based characterization of the tissue microstructure. CLINICAL IMPORTANCE The microstructure map provides a novel insight into the peritumoral microenvironment using a measure that can be derived from clinically feasible DTI data, replacing pre-existing DTI measures.

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